Thor Gone Wild: 3 Concepts, 7 Red Flags, & 5 Lessons of Complexity

Control & Chaos, part 2

by Nick Horton

“Nature abhors a gradient.” — Eric Schneider

The ground rumbled as the thunder cracked. The neuronal-fingers stretched themselves out over the sky like a living web haunting the earth with an eye-splitting brightness. And a single firefly danced in binary, as oblivious to the spectacle as we were enthralled by it.

The Rocky Mountains are notoriously lightning-prone. In fact, the area where I live in western North Carolina has one of the highest death-by-lightning risks in the entire country.¹

So naturally, a few months ago, during a particularly bad storm that knocked out our power, Tamara, the kids, and I all sat on the front porch to watch the electricity light up the sky and see what Ben Franklin made such a fuss about.

Lord of the Fire Flies

While the mountains here wouldn’t qualify as anything but hills back where I grew up in the North West, they are high enough that the lightning and clouds feel like they are only a few feet above your head.

Back in the North West we have huge mountains, massive rivers, and bigger bears, but we don’t have anything like this: neither the lightning nor the lightning bugs!

There in the middle of our yard, with lighting and thunder blasting all around us, a single firefly was busy making a kind of lightning of its own, unaware of how futile its efforts were. I wondered if it would have been different had the little bug had more of its friends around.

Not too far from here, there are species of firefly who, quite without the aid of the Thunder God, are able to light up the night sky by synchronizing their flashing behinds — they will give off a big burst of light, all at once, in unison.

How do the fireflies know how to do that? How do they know how to light up at (nearly) exactly the same time as one another?

Here’s a distillation of one theory:

• Firefly Leader: OK, when I count to 3, everyone light up!
• Firefly Homer: Do we go when you say “3” or when you say “go”?
• Firefly Leader: When I say “3”. Everyone ready? Good. Get ready… one, two, THREE!
• (Everyone goes, except Homer.)
• Firefly Homer: Doh!
• Firefly Female: I choose the Leader…

Humans, with our over-sized brains, find it surprisingly hard to keep ourselves in sync on anything. For instance, musicians need to spend years developing the talent, and much time practicing with their band-mates to ensure that one of them doesn’t end up out of time because it only takes one Homer to ruin the song!

Yet fireflies are capable of acting as members of a grand symphony, a true electric-light orchestra, producing a show that is every bit as wonderful and far more mysterious than the lightning storm we spend that evening watching.

Where did this behavior come from? One possibility is sexual selection. A recent paper, titled Competition and cooperation in a synchronous bush-cricket chorus², looked at the possibility that the evolutionary basis for the development of synchronicity comes down to mate-selection by females. Another paper, titled Firefly Synchrony: A Behavioral Strategy to Minimize Visual Clutter³, tested something similar and came to similar conclusions.

Females are choosing mates (at least in part) based upon how close (in time) to the leader a male is able to light up — giving new meaning to the concept of a quick-draw. We need to be careful, however! This does NOT imply that there is a true “leader” (like a conductor), but rather males vying to be first, or damned-close to it. Think “leader of a race” not “leader of the gang.”

Whatever the reason for its emergence as a behavioral pattern, the fireflies are reacting to signals sent from one another, and the group as a whole exhibits an emergent property based upon this communication and to their relationships with each other.

Neurotic Neurons: Substantia Innominata

The neurons in your brain are like the fireflies: they synchronize, interact, communicate, and adapt. However, unlike the relatively small number of bugs in your back yard, your brain has about 100 billion “actors” (nerve cells) — and the emergent property that comes out of their interaction is what you call your Mind.⁴

Your neurons are members of a complex society of neurons, a network of little cells, each with a “mind” of its own — each cell is a living entity in its own right. Each cell connects with (communicates with) between 1,000 and 10,000 others. And their interactions form the most complex and powerful computer so far found in the universe (we haven’t met the Vulcan’s yet and scanned their superior brains).⁵

The human brain is remarkably complex, but it’s made up of equally simple cells. Each neuron (brain cell) is made up of only three core parts:

• Soma: the cell body.
• Dendrites: the branches that receive the input from other neurons.
• Axon: the single trunk that sends the output from the neuron.

Axons are quite long, and the overall look of a neuron is very tree-like with the soma sitting like a squirrel’s nest in the middle at the top of the axon. The dendrites receive signals from the axons of other neurons. The flow of information is always one-directional.

A neuron, like a firefly, is either (roughly) in an “on” or “off” state. It is either firing or it isn’t. It fires when it receives enough input, coming through its dendrites, from other neurons, reaching a threshold.

When it fires, it releases an electrical pulse through its axon, which is converted into a chemical signal (called a neurotransmitter). The neurotransmitters are what the other neurons will receive as input. Firing frequency and sensitivity to neurotransmitters is something that varies in a neuron over time based on input and how much firing its been doing lately.

Speed of transmission (communication signals) varies a lot between axons and dendrites. For instance, in the cerebral cortex, axons can send signals from between one and thirty meters-per-second — that’s damned fast, in case you’re wondering! Think about how long it takes world-class sprinters to go just 100 meters — and dendrites can receive them at a speed of about one-third of a meter-per-second (substantially slower, because of how much smaller they are).

3 Concepts of Complexity

Fireflies and neurons exhibit remarkably similar group-level behaviors, in spite of the fact that they aren’t alike at all in rather obvious ways⁶.

What matters is that the group shares properties associated with complex adaptive systems. A good summary of complexity, used by the complexity researcher, Melanie Mitchell⁷, is the following in 3-parts:

1. Complex Collective Behavior

Each “actor” (firefly, neuron) follows relatively simple rules, without a central authority (our Homer example is misleading!) giving rise to “the complex, hard-to-predict, and changing patterns of behavior that fascinate us.”

2. **Signaling & Information Processing

Each actor produces signals and processes information from both their internal and external environments.

3. Adaptation

The system adapts via the learning and “updating” of each actor, changing their behavior, to increase their odds of survival or success.

Side Note on Adaptive vs Non-Adaptive Systems

I will follow Mitchell’s lead, and I won’t bother to use the more technically correct term, Complex Adaptive Systems, when discussing the systems we’re interested in, because all of the ones we care about are adaptive: brain, body, society, etc.

Non-adaptive complex systems, like Tsunamis, hurricanes, etc., lack something essential that adaptive systems have: the actors in adaptive systems are ALIVE. (In a tsunami, the “actors” are just particles of inanimate matter.)

7 Red Flags of a Complex System

I said in the past, the phrase “I know it when I see it,” is not a good answer to anything. Unfortunately, because complexity theory it’s such a new field, we’re stuck with definitions that are not too far off from exactly that!

Thankfully there are some general principles that are beginning to gel together.

Below are seven key features, we can think of them as red flags, of a complex system. In other words, these are things you can expect to see in any complex system — not necessarily all of them for sure, but at least a strong subset of them.

So if you’re dealing with a system, and you’re noticing it exhibits a number of these characteristics, that may be a reason for you to say, “Ah ha! I know it when I see it… and this is it!”

Warning!

I think this is a moment where we need to take a step backwards, and be a bit cautious. We never want to get ourselves into a situation where every problem we see looks like a nail, because the only solution we have at our disposal is a hammer.

One of the purposes of last months article in the series, was to make a very clear distinction between chaotic systems and complex systems. Some systems are deterministic, some are not. And it really matters which is which when you’re looking for a solution.

Deterministic systems, chaotic systems, for instance, are not predictable in the long run only because of the computational complexity, because we don’t have the tools at our disposal and to keep track of all of the elements. But that doesn’t mean that in the future we might not be able to. There are some systems we may be able to get a very strong hold on.

Complex systems, indeterministic systems, our non-predictable in their nature. Because of the problem of emergence, it isn’t ever going to be possible to take a completely reductionist view and still be able to solve the problem.

In other words, reductionism works very well when you’re dealing with a chaotic, deterministic system. Whereas holism becomes central when you’re dealing with an indeterministic, complex system.

You Might Be A Complex System If…

Here are the 7 Red Flags that might mean you’re dealing with a complex system (much like the “you might be a red-neck” jokes).

The details of all of these will be explained as we move forward in this series. However, the first article in the last issue covers most of them.

1. Synchronicity: Like the Fireflies. Agents acting as a unit even though they are otherwise individuals (or particles, etc).

2. Fractals: Self-similarity at all levels. Makes for some awesome looking pictures!

3. Power Laws, 1/f Noise, and Regularity of Catastrophes: Imagine earth quakes in Japan. Small tremors regularly, massive disasters periodically: both of which are inevitable.

4. Criticality: a point at which massive change toward order or away from it can happen.

5. Self-Organization: Otherwise random action coordinating so much that we can begin looking at the group as a unit.

6. Emergence: properties of the group that cannot be explained by reducing the analysis down to the individuals. (Economics, evolution, mind, body, etc are all examples that exhibit emergent behaviors.)

7. Amplification of Small Events Over Time: A small change now can result in massive change later. If you’ve ever attempted the caber toss in the Highland Games, the small movements you make with your hands holding it at the bottom can result in a huge change in where the top of the pole moves through the air above! That’s a spacial analogy for what is happening over time.

5 Lessons from Complexity Theory

If it turns out you are dealing with a complex system — you are — then what can you do about it? Here are 5 lessons.

1 – Forest AND Trees

Both holistic and reductionistic analysis is essential in science, certainly in complexity theory. You can’t just have one and ignore the other. The habit of regular-folk (non-scientists) is to pick one or the other.

For instance, is a table a table, or just a collection of atoms?

• We can’t see what it really is, man. The real world is beyond our comprehension. (Usually much weed smoking ensues.)
• It’s a table, dude, and you’re about to bump into it.

In truth: a table is both, and more.

2 – Communication Breakdown

Given that a complex system is defined by the nature of the interaction between the component-parts of that system — networks within networks — communication becomes of key importance between those elements if you want to maximize efficiency.

Micheal Shirmer calls this, “Contingency working with necessity,” that is, probability and disorder working with choice and order.

Which is closely related to…

3 – Criticality Matters

At the critical points, sudden changes occur, states can instantly move into either a deeper level of true chaos, or can self-organize, through emergence, and become more ordered.

The key idea is that emergence is about relationships, which was why communication is so important (as it is in all relationships!).

4 – Prediction FAIL

A complex system can only be predicted in the very near term! (Like a 3-day weather forecast.) In the long run, the predictions cease to be quantitative and become qualitative.

That is, instead of being capable of predicting a specific result that we can put a number on, we can only say things vaguely. “Winter will be colder than summer,” rather than, “On Tuesday, seven months from now, it will be 43 degrees in the afternoon.” The first statement may turn out to be accurate, but it’s scientific value is substantially diminished.

5 – Everything Matters, Nothing Matters

Because of amplification, small events can become deeply important — but only the RIGHT small events! Everything else ends up becoming noise — false positives/negatives.

• First, make sure you dig down and figure out what the essentials ACTUALLY are.
• Then, focus on the essentials and work them HARD.
• Ignore everything else! (This goes quadruple for weightlifting!)

Our friend (Tamara’s old Weightlifting coach) Jim Moser has a saying, “Make the lift equals good technique. Miss the lift equals bad technique. Everything else is noise.”

Without knowing what to call it, he was literally describing how to control a complex system (your body in a weightlifting contest).

Conclusion

In coming issues in this series we’re going to start looking into probability, game theory, and the various areas of science and mathematics that make up the family we collectively call complexity theory. Interestingly, even complexity theory — as a field of science — exhibits properties of a complex adaptive system!

Footnotes